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Staff Machine Learning Engineer

Weekday AI

Responsibilities

  • Lead architecture reviews and establish best practices for scalable AI systems.
  • Design, develop, and deploy large-scale ML solutions in production environments.
  • Data acquisition and exploration
  • Production deployment

Monitoring, governance, and continuous improvement

  • Develop frameworks and reusable components to accelerate ML development across teams.
  • Establish model governance, explainability, fairness, and compliance standards.

Fine-tuning and model adaptation pipelines

  • Lead experimentation and adoption of emerging AI technologies to create competitive advantage.
  • Architect scalable ML platforms and infrastructure.
  • Build and optimize end-to-end ML pipelines.
  • Partner with Product Managers, Engineering leaders, and Business stakeholders to identify high-impact AI opportunities.
  • Translate business problems into scalable AI solutions.
  • Define success metrics and measure business impact.

Requirements

  • Strong expertise in Python, SQL, and distributed computing frameworks such as Spark.
  • Deep knowledge of machine learning and deep learning frameworks:
  • PyTorch
  • TensorFlow

Preferred

  • Reinforcement Learning concepts
  • AWS, Azure, or GCP

Staff Machine Learning Engineer

Weekday AI · Bengaluru, India · Full-time · Posted 2026-09-22

Description

  • This role is for one of Weekday’s clients
  • Min Experience: 10+ years

Location: Bengaluru

JobType: full-time

We are seeking a highly experienced Staff Machine Learning Engineer to lead the architecture, development, and scaling of enterprise-grade Machine Learning and Generative AI platforms.

As a senior technical leader, you will drive AI strategy, establish engineering best practices, mentor ML engineers, and collaborate cross-functionally with Product, Engineering, Data, and Business stakeholders to deliver measurable business outcomes. You will play a critical role in shaping our AI roadmap and building intelligent products that impact thousands of businesses globally.

The ideal candidate will have 8+ years of experience building and deploying large-scale ML systems, deep expertise across the full ML lifecycle, and hands-on experience delivering production-grade Generative AI solutions at scale.

Technical Leadership

  • Define and drive the technical vision for Machine Learning and Generative AI initiatives.
  • Mentor and guide ML engineers and data scientists across teams.
  • Influence product strategy through AI-driven innovation and technical thought leadership.
  • Partner with Engineering leadership to build scalable, reliable, and secure AI platforms.

Machine Learning & Data Science

  • Build advanced predictive models, recommendation systems, forecasting solutions, NLP applications, and deep learning systems.
  • Drive the complete machine learning lifecycle:
  • Problem definition
  • Feature engineering
  • Model development
  • Model evaluation and validation

Generative AI & LLM Applications

  • Architect and deliver enterprise-scale GenAI solutions leveraging:
  • OpenAI
  • Azure OpenAI
  • Anthropic Claude
  • Llama
  • Mistral
  • Gemini

Design and implement

  • Advanced RAG architectures
  • Agentic AI systems
  • Multi-agent workflows
  • AI orchestration frameworks
  • Prompt engineering and evaluation frameworks
  • Knowledge graph-assisted AI systems
  • AI observability and evaluation frameworks

Drive MLOps best practices including

  • CI/CD for ML
  • Model serving
  • Feature stores
  • Experiment tracking
  • Monitoring and observability
  • Automated retraining pipelines

Model governance and security

Optimize system performance, scalability, reliability, and cost efficiency.

Cross-Functional Collaboration

Drive AI adoption and technical excellence across the organization.

Experience

  • 10+ years of experience in Machine Learning, Data Science, and AI Engineering.
  • Proven track record of delivering production-grade AI/ML products at scale.
  • Experience leading complex technical initiatives and influencing engineering direction.
  • Experience mentoring engineers and driving technical excellence across teams.

Technical Skills

Scikit-learn

Strong expertise in

  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)
  • Agentic AI Systems
  • AI Evaluation Frameworks

Hands-on experience with

  • Docker
  • Kubernetes
  • Vector Databases
  • API and Microservices Architecture

Expertise in

  • MLOps
  • Model Deployment
  • Feature Stores
  • Experiment Tracking
  • Observability and Monitoring

Leadership Attributes

  • Strong architectural and systems-thinking mindset.
  • Ability to influence without authority and drive cross-functional alignment.
  • Exceptional communication and stakeholder management skills.
  • Passion for mentoring, innovation, and continuous learning.

Must-have skills

Applied Machine Learning

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